Deep-Learning Aided Channel Training and Precoding in FDD Massive MIMO with Channel Statistics Knowledge

We propose a method for channel training and precoding in FDD massive MIMO based on deep neural networks (DNNs), exploiting Downlink (DL) channel covariance knowledge. The DNN is optimized to maximize the DL multi-user sum-rate, by producing a pre-beamforming matrix based on user channel covariances...

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Vydané v:IEEE International Conference on Communications (2003) s. 2791 - 2797
Hlavní autori: Song, Yi, Yang, Tianyu, Khalilsarai, Mahdi Barzegar, Caire, Giuseppe
Médium: Konferenčný príspevok..
Jazyk:English
Vydavateľské údaje: IEEE 28.05.2023
Predmet:
analog feedback
Artificial neural networks
Channel estimation
channel statistics knowledge
DNN-based training and precoding
FDD massive MIMO
Knowledge engineering
Microprocessors
Precoding
Scattering
Training
ISSN:1938-1883
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Popis
Shrnutí:We propose a method for channel training and precoding in FDD massive MIMO based on deep neural networks (DNNs), exploiting Downlink (DL) channel covariance knowledge. The DNN is optimized to maximize the DL multi-user sum-rate, by producing a pre-beamforming matrix based on user channel covariances that maps the original channel vectors to "effective channels". Measurements of these effective channels are received at the users via common pilot transmission and sent back to the base station (BS) through analog feedback without further processing. The BS estimates the effective channels from received feedback and constructs a linear precoder by concatenating the optimized pre-beamforming matrix with a zero-forcing precoder over the effective channels. We show that the proposed method yields significantly higher sum-rates than the state-of-the-art DNN-based channel training and precoding scheme, especially in scenarios with small pilot and feedback size relative to the channel coherence block length. Unlike many works in the literature, our proposition does not involve deployment of a DNN at the user side, which typically comes at a high computational cost and parameter-transmission overhead on the system, and is therefore considerably more practical.
ISSN:1938-1883
DOI:10.1109/ICC45041.2023.10279459